Verifiable AI Data: Why It’s Critical for the Automation Revolution

In this special guest feature, Dirk Kanngiesser, co-founder and CEO of Cryptowerk, discusses how vendors implement Verifiable AI into their products to ensure that their AI algorithms are not handling data that has been tampered with, and how companies build Verifiable AI into their systems to verify that they are using safe data. Cryptowerk is a provider of data integrity solutions using blockchain technology. With more than 25 years of technology leadership experience, Dirk has led new product and business launches at multiple companies in both the U.S. and Europe.

Businesses are increasingly using AI to automate processes to gain efficiencies, be more competitive and avoid disruption in their markets. According to a recent Accenture survey, 82% of executives say that their organizations are using data and algorithms based on that data to drive critical and automated decision-making at unprecedented scale.

But what
if the data entering the AI algorithms has been compromised along the way, or
the algorithms themselves altered?

Admiral Michael S. Rogers of the U.S. Navy and Director of the NSA has said that data tampering could become the greatest cybersecurity threat organizations face. Kissinger Associates CEO Jami Miscik agrees that a more damaging brand of cyberattack would be where perpetrators don’t steal the data — but change it! She offered potential examples of companies not knowing where their inventories are, or financial services firms having the balances changed in their accounts and noted that such tactics could wreak havoc for businesses and the broader economy at a time when there is already flagging confidence in the media and the political system.

One prime example comes from the oil industry.
If hackers access data sources and attempt to shut down an oil rig for a few
days, it causes business disruption and lost productivity. But what if those
same hackers covertly alter the data, and the oil company spends months
drilling in the wrong places? That’s an enterprise-level existential threat.

In an age of manipulated data, deepfake
techniques, and unending data breaches, companies need to know that they are
using pristine data in their AI systems. Toxic data will wreak havoc in
business systems and cripple an organization’s faith in AI and its customers’
faith in it. Before understanding how and why decisions were made,
organizations must be able to stand by the integrity of the data and algorithms
used by AI. This might be called Verifiable
AI — when an organization can provide immutable proof that the data used
by their AI systems is unaltered.

So how do vendors implement Verifiable AI into
their products to ensure that their AI algorithms are not handling data that
has been tampered with? And how do companies build Verifiable AI into their systems to verify that
they are using safe data?

Two leading pharmaceutical companies — Merck and Company, Inc. and Amerisource Bergen — have implemented a fast-track project to more efficiently detect counterfeits and irrefutably document compliance at scale. The project is designed to Track and Trace billions of items throughout their supply chains. In just a few weeks they were able to ensure:

Full regulatory compliance around sellable return verifications

Instant verification of the authenticity of returned items

No replication of manufacturer data required for wholesalers

Minimal complexity, maximal security

Immutable, single source of truth provided to all parties, including regulators

Interoperable with existing Track and Trace solutions

Scalable-to-consumer scanning at the point of dispense

With this
project, these companies were able to achieve full data verification at
multiple stages across their supply chain. By extending the real-time Track and
Trace ability, they are now able to add a new “Train” element into the process.
The verification that is used to ensure that their supply chain is not handling
counterfeit goods can also be used to verify that data used to train AI systems
for supply chain automation is also safe. For these companies, Track, Trace and Train is an additional benefit of a chain of custody
solution, but for many other companies, this will be their core safeguard of
their AI systems.

Data
tampering is now the single greatest cybersecurity threat that organizations
face. From a simple act of revenge by a disgruntled employee, to corporate
espionage, or even a nation-state attack, data tampering is an existential
threat that cannot be ignored. While AI automation holds the promise of
operational efficiencies, it also has the ability to introduce and replicate toxic
data across an enterprise, into business systems and decision-making, and
expose customers and partners to those very same risks.

The AI automation revolution is already upon us. But its success hinges on the authenticity of the data that drives the AI, and the ability to verify this authenticity.

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In this special guest feature, Brian D’alessandro, Director of Data Science at SparkBeyond, discusses how AI is a learning curve, and exploring opportunities within the technology further extends its potential to enable transformation and generate impact. It can shape workflows to drive efficiency and growth opportunities, while automating other workflows and create new business models. While AI empowers us with the ability to predict the future — we have the opportunity to change it. [READ MORE…]

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